图像处理软件Adobe Photoshop和Adobe Illustrator在昆虫绘图及图像处理中的应用

本文介绍了图像处理软件Adobe Photoshop和Adobe Illustrator在昆虫学中的若干应用技巧及注意事项,从而为该类软件在昆虫学研究工作中的使用及推广提供更多的思路。     

计算机辅助教学软件——果蝇遗传实验的计算机演示

(一)程序简介随着微型电子计算机的普及,计算机在教学中的应用越来越广泛,下面介绍一个可以在课堂上演示的模拟果蝇遗传学实验的程序。程序使用的机型是Apple-II(苹果-II),机上配有“佳佳”汉字卡,程序使用的语言为BASIC语言。程序通过键盘来选择实验果蝇的遗传性状,可供选择的遗传性状有三对,性状选择好了之后,就由计算机完成其实验,在显示中不但有汉字的文字说明、比例等,也有果蝇的彩色图     

三维技术在古昆虫复原图制作中的应用

本文简单介绍了Autodesk Maya等软件以及利用三维技术制作古昆虫复原图所涉及到的三大方面知识,包括:三维昆虫制作、场景设计和艺术气氛。较为详细的介绍了利用三维软件制作昆虫的主要步骤。总结了在制作复原图时,将生物学、计算机技术、美术学三方面知识相结合的经验和技巧。为三维技术在古昆虫学研究工作中的推广和使用提供了更多思路。     

用计算机进行昆虫分类检索研究初探

本文采用昆虫分类上通用的两项式分类法,用计算机进行昆虫分类检索,详尽地介绍了检索原理、程序框图、输入资料的方法及修改资料的命令,并以检索夜蛾科成虫为例,对操作步骤和使用方法及程序中的主要变量进行了解释。     

生物技术软件介绍之二

笔者在《生命科学》1992年第2期上发表的《生物技术软件介绍之一》介绍了6类9个生物技术数据库和软件,包括细胞学数据库、核酸和蛋白质序列数据库和序列分析软件、PCR引物程序等。近一年多来,我们又引进和开发一批新的生物技术软件,其中有些是我们自行设计,有些购自国外,有些通过与国内外学者进行软     

变革实验报告写作 提升实验教学效果

简介实验工作单,分析了实验工作单的价值,以"观察植物细胞的质壁分离与复原"实验为例,介绍了使用实验工作单的一般程序,并提出了工作单使用中应注意的问题。     

PCR引物设计及软件使用技巧

介绍了使用软件设计PCR引物的技巧。在PCR引物设计原则的基础上 ,详细介绍了两种常用引物设计软件的基本使用方法 ,并对其各自的优缺点进行了比较。一般性引物自动搜索可采用“PremierPrimer 5”软件 ,而引物的评价分析则可采用“Oli go6”软件。     

计算机技术在群纱表制作和群落分类中的应用

介绍利用Microsoft Foxpro和Excel软件编写程序,实现法瑞学派植物群落分类方法中群落表制作各个步骤的新技术,说明了制表程序的运行程序,并规定了该程序所需要的数据格多。这一技术简化了传统制表过程。本文提供了一个利用此新技术对33个样地进行制表和群落分类的实例。     

生物软件在序列分析过程中的运用

介绍了组合使用BLAST、FASTA/BLASTScan3.2,或用多序列比对软件,从数据库中快速提取大数量目标序列,最后用MEGA4快捷编辑整理大数量序列的方法。还介绍了一种生成核酸序列与其氨基酸序列相似性百分率整合表格的方法。简述了对引物设计的基本认识并介绍了多重引物兼容性筛选软件;对构建系统发育树的认识并引出分子进化树构建软件MEGA4的使用和PAUP 4.0常用建树命令模块。期望这些方法和软件的使用能解决生物序列分析过程的常见问题。     

蛋白质紫外测定数据的微机分析

本文叙述了以波长280nm为中心的蛋白质紫外测定数据微机的分析程序,应用本文程序测算了几种生物样品中的蛋白含量及核酸总量。程序使用BASIC语言编制,在KC-805微机上通过。     

Ten simple rules for finding and selecting R packages

R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R’s utility and ameliorate inefficient solutions to data science problems. We outline 10 simple rules for finding relevant packages and determining which package is best for your desired use. We begin in Rule 1 with tips on how to consider your purpose, which will guide your search to follow, where, in Rule 2, you’ll learn best practices for finding and collecting options. Rules 3 and 4 will help you navigate packages’ profiles and explore the extent of their online resources, so that you can be confident in the quality of the package you choose and assured that you’ll be able to access support. In Rules 5 and 6, you’ll become familiar with how the R Community evaluates packages and learn how to assess the popularity and utility of packages for yourself. Rules 7 and 8 will teach you how to investigate and track package development processes, so you can further evaluate their merit. We end in Rules 9 and 10 with more hands-on approaches, which involve digging into package code.     

V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants

We present V.PhyloMaker, a freely available package for R designed to generate phylogenies for vascular plants. The mega‐tree implemented in V.PhyloMaker (i.e. GBOTB.extended.tre), which was derived from two recently published mega‐trees and includes 74 533 species and all families of extant vascular plants, is the largest dated phylogeny for vascular plants. V.PhyloMaker can generate phylogenies for very large species lists (the largest species list that we tested included 314 686 species). V.PhyloMaker generates phylogenies at a fast speed, much faster than other phylogeny‐generating packages. Our tests of V.PhyloMaker show that generating a phylogeny for 60 000 species requires less than six hours. V.PhyloMaker includes an approach to attach genera or species to their close relatives in a phylogeny. We provide a simple example in this paper to show how to use V.PhyloMaker to generate phylogenies.     

A practical guide to MaxEnt for modeling species’ distributions: what it does,and why inputs and settings matter

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.     

Calculating autoecological data (optima and tolerance range) for multiple species with the ‘optimos.prime’ R package

The calculation of autoecological data, such as optima and tolerance ranges to environmental variables, can be useful to establish the distribution and abundance of the species. These calculations, although mathematically not complex, can be prone to error when using a large database. We show how to calculate the optimum value and tolerance ranges of multiple species to multiple environmental factors in a single run, by weighted average using a specific R package (‘optimos.prime’). Using sample data from a phytoplankton database, we exemplify the use of the R package and its functions. A stand‐alone version for Windows is also provided, and source code and documents are freely available on GitHub to encourage collaborative work.     

affy--analysis of Affymetrix GeneChip data at the probe level

MOTIVATION: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. RESULTS: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.     

Generation of synthetic data and experimental designs in evaluating interactions for association studies

Complex diseases, by definition, involve multiple factors, including gene-gene interactions and gene-environment interactions. Researchers commonly rely on simulated data to evaluate their approaches for detecting high-order interactions in disease gene mapping. A publicly available simulation program to generate samples involving complex genetic and environmental interactions is of great interest to the community. We have developed a software package named gs1.0, which has been widely used since its publication. In this article, we present an upgraded version gs2.0, which not only inherits its capacity to generate realistic genotype data but also provides great functionality and flexibility to simulate various interaction models. In addition to a standalone version, a user-friendly web server (http://cbc.case.edu/gs) has been set up to help users to build complex interaction models. Furthermore, by utilizing three three-locus models as an example, we have shown how realistic model parameters can be chosen in generating simulated data.     

Eradication of feral goats on Guadalupe Island: absence confirmation

Insular ecosystems all over the world have been negatively affected by the introduction of ungulates, and it is due to the dramatic impacts that extensive control/eradication programmes have been undertaken. Eradication programmes using sophisticated techniques were recently carried out on Mexican islands. However, the phase of absence confirmation is facing the challenges seen elsewhere: how much effort is needed to confirm success? We present the case of Guadalupe Island in the Mexican Pacific. Eradication of feral goats (Capra hircus) was conducted on this volcanic island of 250 km2 from 2002-2006. Since 2007 we have focussed on confirming absence through use of Judas goats. We discuss our results to date and how future assessments can be improved.     

CNVassoc: Association analysis of CNV data using R

Background

Copy number variants (CNV) are a potentially important component of the genetic contribution to risk of common complex diseases. Analysis of the association between CNVs and disease requires that uncertainty in CNV copy-number calls, which can be substantial, be taken into account; failure to consider this uncertainty can lead to biased results. Therefore, there is a need to develop and use appropriate statistical tools. To address this issue, we have developed CNVassoc, an R package for carrying out association analysis of common copy number variants in population-based studies. This package includes functions for testing for association with different classes of response variables (e.g. class status, censored data, counts) under a series of study designs (case-control, cohort, etc) and inheritance models, adjusting for covariates. The package includes functions for inferring copy number (CNV genotype calling), but can also accept copy number data generated by other algorithms (e.g. CANARY, CGHcall, IMPUTE).

Results

Here we present a new R package, CNVassoc, that can deal with different types of CNV arising from different platforms such as MLPA o aCGH. Through a real data example we illustrate that our method is able to incorporate uncertainty in the association process. We also show how our package can also be useful when analyzing imputed data when analyzing imputed SNPs. Through a simulation study we show that CNVassoc outperforms CNVtools in terms of computing time as well as in convergence failure rate.

Conclusions

We provide a package that outperforms the existing ones in terms of modelling flexibility, power, convergence rate, ease of covariate adjustment, and requirements for sample size and signal quality. Therefore, we offer CNVassoc as a method for routine use in CNV association studies.     

Waste Not,Want Not: Why Rarefying Microbiome Data Is Inadmissible

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.     

An assessment of scientific approaches towards species relocations in Australia

Species relocation programmes are increasingly performed with the intention of establishing a self‐sustaining population of threatened or declining native species. However, the use of experimental quantitative approaches in species relocation programmes is still relatively uncommon, despite a number of international studies recommending clear guidelines and standards. This paper evaluates species relocation programmes conducted within Australia to assess how programmes performed in relation to such standards. The search techniques identified 54 species relocation programmes, the majority of which were reintroductions (52%) and supplementations (30%). Only 25 (46%) of the species relocation programmes claimed success, with a lack of effective predator control recognized as contributing to the failure of 14 programmes. There was considerable variation in the quality of species relocation programmes in relation to key features such as whether the programme integrated experimental approaches with testable hypotheses, whether there were explicit statements of criteria for success, whether suitable habitat was identified for the release site and whether long‐term monitoring was conducted. We propose guidelines to improve scientific rigour and success rates of species relocation programmes.